Big Multidimensional Datasets Visualization Using Neural Networks – Efficient Decision Support

Gintautas Dzemyda, Viktor Medvedev, Audrone Lupeikiene, Olga Kurasova, Albertas Caplinskas

Abstract


Nowadays business information systems are thought of as decision-oriented systems supported by different types of subsystems. Multidimensional data visualization is an essential part of such systems. As datasets tend to be increasingly large, more effective ways are required to display, analyze and interpret information they contain. Most of the classical visualization methods are unsuitable for large datasets. This paper focuses on the artificial neural networks-based methods for visualization of big multidimensional datasets; namely,  on the approaches for the faster obtaining of visual results. The new strategy, which is identified by the decreased number of cycles of data reviews (passes of training data) up to the only one, when training neural networks, is proposed. To test this strategy, the results of experiments, using two unsupervised learning methods on benchmark data, are briefly presented.

Keywords:

Data visualization; big multidimensional dataset; neural networks-based method; decision support

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DOI: 10.7250/csimq.2016-6.01

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